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Chuanren Liu

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8 papers

1 author row

IJCAI Conference 2021 Conference Paper

GraphMI: Extracting Private Graph Data from Graph Neural Networks

Zaixi Zhang
Qi Liu
Zhenya Huang
Hao Wang
Chengqiang Lu
Chuanren Liu
Enhong Chen

As machine learning becomes more widely used for critical applications, the need to study its implications in privacy becomes urgent. Given access to the target model and auxiliary information, model inversion attack aims to infer sensitive features of the training dataset, which leads to great privacy concerns. Despite its success in the grid domain, directly applying model inversion techniques on non grid domains such as graph achieves poor attack performance due to the difficulty to fully exploit the intrinsic properties of graphs and attributes of graph nodes used in GNN models. To bridge this gap, we present Graph Model Inversion attack, which aims to infer edges of the training graph by inverting Graph Neural Networks, one of the most popular graph analysis tools. Specifically, the projected gradient module in our method can tackle the discreteness of graph edges while preserving the sparsity and smoothness of graph features. Moreover, a well designed graph autoencoder module can efficiently exploit graph topology, node attributes, and target model parameters. With the proposed method, we study the connection between model inversion risk and edge influence and show that edges with greater influence are more likely to be recovered. Extensive experiments over several public datasets demonstrate the effectiveness of our method. We also show that differential privacy in its canonical form can hardly defend our attack while preserving decent utility.

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AAAI Conference 2020 Conference Paper

Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach

Yang Liu
Qi Liu
Hongke Zhao
Zhen Pan
Chuanren Liu

In recent years, considerable efforts have been devoted to developing AI techniques for ﬁnance research and applications. For instance, AI techniques (e. g. , machine learning) can help traders in quantitative trading (QT) by automating two tasks: market condition recognition and trading strategies execution. However, existing methods in QT face challenges such as representing noisy high-frequent ﬁnancial data and ﬁnding the balance between exploration and exploitation of the trading agent with AI techniques. To address the challenges, we propose an adaptive trading model, namely iRDPG, to automatically develop QT strategies by an intelligent trading agent. Our model is enhanced by deep reinforcement learning (DRL) and imitation learning techniques. Speciﬁcally, considering the noisy ﬁnancial data, we formulate the QT process as a Partially Observable Markov Decision Process (POMDP). Also, we introduce imitation learning to leverage classical trading strategies useful to balance between exploration and exploitation. For better simulation, we train our trading agent in the real ﬁnancial market using minute-frequent data. Experimental results demonstrate that our model can extract robust market features and be adaptive in different markets.

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IJCAI Conference 2020 Conference Paper

Why We Go Where We Go: Profiling User Decisions on Choosing POIs

Renjun Hu
Xinjiang Lu
Chuanren Liu
Yanyan Li
Hao Liu
Jingjing Gu
Shuai Ma
Hui Xiong

While Point-of-Interest (POI) recommendation has been a popular topic of study for some time, little progress has been made for understanding why and how people make their decisions for the selection of POIs. To this end, in this paper, we propose a user decision profiling framework, named PROUD, which can identify the key factors in people's decisions on choosing POIs. Specifically, we treat each user decision as a set of factors and provide a method for learning factor embeddings. A unique perspective of our approach is to identify key factors, while preserving decision structures seamlessly, via a novel scalar projection maximization objective. Exactly solving the objective is non-trivial due to a sparsity constraint. To address this, our PROUD adopts a self projection attention and an L2 regularized sparse activation to directly estimate the likelihood of each factor to be a key factor. Finally, extensive experiments on real-world data validate the advantage of PROUD in preserving user decision structures. Also, our case study indicates that the identified key decision factors can help us to provide more interpretable recommendations and analyses.

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AAAI Conference 2019 Conference Paper

Exploiting the Contagious Effect for Employee Turnover Prediction

Mingfei Teng
Hengshu Zhu
Chuanren Liu
Chen Zhu
Hui Xiong

Talent turnover often costs a large amount of business time, money and performance. Therefore, employee turnover prediction is critical for proactive talent management. Existing approaches on turnover prediction are mainly based on profiling of employees and their working environments, while the important contagious effect of employee turnovers has been largely ignored. To this end, in this paper, we propose a contagious effect heterogeneous neural network (CEHNN) for turnover prediction by integrating the employee profiles, the environmental factors, and more importantly, the influence of turnover behaviors of co-workers. Moreover, a global attention mechanism is designed to evaluate the heterogeneous impact on potential turnover behaviors. This attention mechanism can improve the interpretability of turnover prediction and provide actionable insights for talent retention. Finally, we conduct extensive experiments and case studies on a realworld dataset from a large company to validate the effectiveness of the contagious effect for turnover prediction.

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AAAI Conference 2018 Conference Paper

Confidence-Aware Matrix Factorization for Recommender Systems

Chao Wang
Qi Liu
Runze Wu
Enhong Chen
Chuanren Liu
Xunpeng Huang
Zhenya Huang

Collaborative ﬁltering (CF), particularly matrix factorization (MF) based methods, have been widely used in recommender systems. The literature has reported that matrix factorization methods often produce superior accuracy of rating prediction in recommender systems. However, existing matrix factorization methods rarely consider conﬁdence of the rating prediction and thus cannot support advanced recommendation tasks. In this paper, we propose a Conﬁdence-aware Matrix Factorization (CMF) framework to simultaneously optimize the accuracy of rating prediction and measure the prediction conﬁdence in the model. Speciﬁcally, we introduce variance parameters for both users and items in the matrix factorization process. Then, prediction interval can be computed to measure conﬁdence for each predicted rating. These conﬁdence quantities can be used to enhance the quality of recommendation results based on Conﬁdence-aware Ranking (CR). We also develop two effective implementations of our framework to compute the conﬁdence-aware matrix factorization for large-scale data. Finally, extensive experiments on three real-world datasets demonstrate the effectiveness of our framework from multiple perspectives.

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IJCAI Conference 2018 Conference Paper

Patent Litigation Prediction: A Convolutional Tensor Factorization Approach

Qi Liu
Han Wu
Yuyang Ye
Hongke Zhao
Chuanren Liu
Dongfang Du

Patent litigation is an expensive legal process faced by many companies. To reduce the cost of patent litigation, one effective approach is proactive management based on predictive analysis. However, automatic prediction of patent litigation is still an open problem due to the complexity of lawsuits. In this paper, we propose a data-driven framework, Convolutional Tensor Factorization (CTF), to identify the patents that may cause litigations between two companies. Specifically, CTF is a hybrid modeling approach, where the content features from the patents are represented by the Network embedding-combined Convolutional Neural Network (NCNN) and the lawsuit records of companies are summarized in a tensor, respectively. Then, CTF integrates NCNN and tensor factorization to systematically exploit both content information and collaborative information from large amount of data. Finally, the risky patents will be returned by a learning to rank strategy. Extensive experimental results on real-world data demonstrate the effectiveness of our framework.

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AAAI Conference 2017 Conference Paper

A Context-Enriched Neural Network Method for Recognizing Lexical Entailment

Kun Zhang
Enhong Chen
Qi Liu
Chuanren Liu
Guangyi Lv

Recognizing lexical entailment (RLE) always plays an important role in inference of natural language, i. e. , identifying whether one word entails another, for example, fox entails animal. In the literature, automatically recognizing lexical entailment for word pairs deeply relies on words’ contextual representations. However, as a “prototype” vector, a single representation cannot reveal multifaceted aspects of the words due to their homonymy and polysemy. In this paper, we propose a supervised Context-Enriched Neural Network (CENN) method for recognizing lexical entailment. To be speciﬁc, we ﬁrst utilize multiple embedding vectors from different contexts to represent the input word pairs. Then, through different combination methods and attention mechanism, we integrate different embedding vectors and optimize their weights to predict whether there are entailment relations in word pairs. Moreover, our proposed framework is ﬂexible and open to handle different word contexts and entailment perspectives in the text corpus. Extensive experiments on ﬁve datasets show that our approach signiﬁcantly improves the performance of automatic RLE in comparison with several state-of-the-art methods.

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IJCAI Conference 2017 Conference Paper

Enhancing Campaign Design in Crowdfunding: A Product Supply Optimization Perspective

Qi Liu
Guifeng Wang
Hongke Zhao
Chuanren Liu
Tong Xu
Enhong Chen

Crowdfunding is an emerging Internet application for creators designing campaigns (projects) to collect funds from public investors. Usually, the limited budget of the creator is manually divided into several perks (reward options), that should fit various market demand and further bring different monetary contributions for the campaign. Therefore, it is very challenging for each creator to design an effective campaign. To this end, in this paper, we aim to enhance the funding performance of the newly proposed campaigns, with a focus on optimizing the product supply of perks. Specifically, given the expected budget and the perks of a campaign, we propose a novel solution to automatically recommend the optimal product supply to every perk for balancing the expected return of this campaign against the risk. Along this line, we define it as a constrained portfolio selection problem, where the risk of each campaign is measured by a multi-task learning method. Finally, experimental results on the real-world crowdfunding data clearly prove that the optimized product supply can help improve the campaign performance significantly, and meanwhile, our multi-task learning method could more precisely estimate the risk of each campaign.

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